Cancer is a wide spread disease that expresses itself through the errant growth of abnormal cells. If the uncontrolled growth of these cells is not stopped, it can be fatal. The fatal trend of cancer worldwide is steadily increasing along with the overall estimated cost for cancer management. This has led to an increased demand from the general public to develop more effective tools and technologies for treating and curing the disease. With the rapid advancement of medical imaging, tumors are being diagnosed in early stages when they are still local or regional. Radiation therapy, brachytherapy, particle therapy, and chemotherapy are effective in treating local cancel cells, or tumors.
Radiation therapy uses ionizing radiation to control or kill malignant cells. Brachytherapy uses radioactive sources to treat malignant cells at short-range. Particle therapy uses beams of energetic protons, neutrons, or positive ions for cancer treatment. Chemotherapy is the invasive use of drugs to target and kill malignant cells.
Radiation therapy is a modality of cancer treatment that uses ionizing radiation (e.g., high energy X-rays) that damages the DNA and causes cell death within the region being irradiated. Hence, the goal of radiation therapy is to deliver a radiation dose high enough to kill all the targeted tumor cells while simultaneously minimizing the damage to surrounding normal structures. The quality of a radiotherapy plan is usually judged by its dose conformity and treatment time. The dose conformity describes how well the high radiation dose region conforms to the targeted tumor and spares the surrounding normal tissues, while treatment time describes how long treatment takes and the efficiency of the treatment machines used. Any improvement in the dose conformity in radiation therapy likely improves tumor control and reduces the likelihood of complications, and any improvement in treatment time likely lowers the treatment cost and improves patient throughput and comfort.
Many types of ionizing radiation have been experimented through the history of radiotherapy. These include high energy photons (e.g., y-rays and high energy X-rays), electrons, and charged particles heavier than electrons such as protons, pions, alpha particles, carbon ions, and even antiprotons.
In order to provide dose conformity and efficient treatment time, radiotherapy relies on specialized optimization algorithms, for instance simulated annealing, deterministic optimization models such as linear programming, non-negative least squares, linear programming, among others; genetic algorithms, neural networks, mixed integer linear programming, and graph algorithms, etc. Usually these algorithms try to model all competing treatment goals and radiation source configurations as a unique optimization problem.
Modern radiation therapy treatment planning typically involves the following set of steps: patient imaging, target definition (i.e., structure contouring), dose prescription, beam configuration optimization, plan generation, and quality assurance.
Imaging is performed by taking computer tomography scans (CT scans), magnetic resonance imaging (MRI), positron emission tomography (PET), ultrasound or combinations of these depending on the type of cancer. CT scan is the most widely used imaging modality and can provide anatomical information of the patient. Once these images are obtained, physicians contour the tumor and organs at risk (OARs) as well as prescribe the desired dose to treat the tumor.
Modern radiation therapy relies on computer based optimization algorithms and software to generate the beam configuration for delivering the prescribed treatment.
Generally speaking, a treatment planning system includes the following functionalities in order to provide an optimal treatment:
(1) Patient Representation in which the computational model of a patient is represented as a three dimensional voxel array with resolution inherited from the type of imaging used (e.g., 1 mm×1 mm×1 mm). While contouring is performed, each voxel is associated with a particular structure which allows the identification of tumor voxels (target) versus organ-at-risk voxels.
(2) Ideal Dose Distribution is aided by the prescription obtained from the physicians and a desired dose distribution is generated. A desired dose distribution usually consists of a maximum and minimum dose tolerance per structure. There are many ways to represent this ideal dose distribution. As an example, the ideal dose distribution is assumed as a two three-dimensional array of dose values corresponding to a maximum and minimum tolerance per structure's voxel.
(3) Preparation for Dose Calculation includes the goal of treatment planning to select a subset of beam configurations (e.g., beam energies, locations, angles, size, etc.) from a set of candidate beam configurations that can meet the ideal or prescribed dose distribution. Beam configurations are usually selected using randomized sampling or by applying other optimization algorithms. Once beam configurations are selected, the dose contribution from each candidate beam configuration is calculated before beam-on time optimization. This fact imposes a constraint on the cardinality of the candidate beam configurations set, since it is a process bounded by the amount of memory RAM available in the system.
(4) The conceptual optimization problem in radiation therapy may be stated as:
                    minimize        t            ⁢                                            ∑                                                            D                  .                                j                            ⁢                              t                j                                              -                      D            *                                      ⁢                          ⁢      subject      ⁢                          ⁢      to      ⁢                          ⁢              t        j              ≥    0    ,where D* is the ideal dose distribution or prescribed dose. Dj is the dose rate contributed by the j-th candidate beam, and tj is the weighting or beam-on time for the j-th beam. The constraint tj≧0 reflects that the beam-on time must be non-negative. Thus the goal of the optimization is to find the beam-on times tj so that the created dose distribution Σj{dot over (D)}j×tj is as close to D* as possible. In certain situations, there may be an additional constraint such as that the total beam-on time must be below a certain threshold. The optimization problem may be viewed as a constrained least square problem.
(5) Plan Generation that processes the optimal result in order to generate a set of instructions compatible with the radiation modality and technology used. The plan is reviewed and tested as part of quality assurance.
Optimization is a fundamental tool in science, and it can be defined as the systematic process to achieve a pre-specified purpose. Generally speaking, the purpose is related to two states: maximize a benefit or minimize an effort. Optimization algorithms are classified according to different aspects of their formulation, for instance, according to the nature of their objective function, they can be linear, non-linear, quadratic, convex, concave, differentiable, non-differentiable, etc. According to the domain of the decision variables, they can be continuous or discrete. In addition, they can be classified according to the method of operation into two groups: deterministic and probabilistic algorithms.
Deterministic algorithms such as least-distance programming, non-negative least squares, least squares, etc. are used when there exists a clear understanding of the characteristics of the possible solutions and their utility for a given problem. When there is not a clear understanding of a possible solution and its utility or the search space has high dimensionality, these algorithms may not succeed in finding a solution. Then, probabilistic algorithms are used. Probabilistic or stochastic algorithms are those that sample the space of possible solutions for a given problem and through heuristics iteratively improve the solution.
The main goal of radiation therapy is to deliver a lethal dose of radiation to the targeted tumor while minimizing the radiation dose to the surrounding normal tissues and critical organs. Modern cancer therapy has benefited enormously from computer controlled treatment devices with increased precision and capability. However, this increased sophistication also creates new challenges for treatment planning. As the number of parameters in a treatment plan increases, the traditional computational approaches are no longer adequate to fully exploit the potential of the latest treatment devices. This is because at the heart of treatment planning is often a set of substantially non-trivial constrained geometric optimization problems.
Thus, there is a need for improved treatment planning using superior optimization techniques to solve for optimal and reliable locations for delivery of radiation doses to a targeted tumor while minimizing the radiation dose experienced by the surrounding critical structures such as normal tissues and organs. The invention satisfies this need.